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This study introduces a real-time system for detecting facial emotions using Convolutional Neural Networks (CNNs). It focuses on identifying key emotional states such as sadness, happiness, fear, anger,
surprise, and neutrality. By applying advanced computer vision methods, the system processes live video feeds to locate and analyze facial regions, which are then classified through a deep learning model. The
CNN was optimized for speed and accuracy, enabling reliable performance on standard computing hardware. To enhance model generalization, datasets like FER-2013 were used, along with techniques
such as image preprocessing and augmentation. OpenCV was integrated for handling video input and facial detection, while TensorFlow/Keras supported emotion classification. The user interface displays
emotion labels on the live video feed, making the tool accessible for practical applications.
This research contributes to affective computing, mental health tools, and intelligent user interfaces by equipping machines with the ability to interpret human emotions. The proposed system has potential uses in education, therapy, customer service, and interactive applications.
Keywords:
Real-time emotion detection, Convolutional Neural Networks, Human computer interaction, Deep learning, Face detection, Affective computing, OpenCV, TensorFlow.
Cite Article:
"Emotion detection", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 6, page no.c28-c33, June-2025, Available :http://www.ijrti.org/papers/IJRTI2506204.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator